caching-strategy-selector

name	caching-strategy-selector
description	caching strategy(Cache Aside, Write Through, Write Behind etc.) optional and Redis/Memcached for, TTL , cache invalid-ize pattern guide. 'caching strategy', 'Redis', 'cache invalid-ize', 'TTL', 'Cache Aside', 'Write Through', 'CDN caching', 'cache ' etc. caching this for. optimization-engineerof caching -ize. , before performance profilingthis benchmark this of scope .

Caching Strategy Selector — caching strategy optional guide

-based caching strategy optionaland and-basedas lower methodology.

caching strategy

1. Cache Aside (Lazy Loading)

: App → Cache confirmation →  → DB query → Cache  → 
: App → DB  → Cache invalid-ize(deletion)

for-based	request upper
cache DB	-modification- cases
necessary dataonly cache	data day also

2. Write Through

: App → Cache  → DB  (synchronous)
: App → Cache confirmation → upper 

- consistency	latency
cache upper	forlower dataalso cache

3. Write Behind (Write Back)

: App → Cache  → asynchronous as DB 
: App → Cache confirmation → upper 

performance -ize	cache data
DB lower -ize	complex

strategy optional

requiredmatter	Cache Aside	Write Through	Write Behind
	★★★	★★	★★
	★	★	★★★
consistency important	★★	★★★	★
performance important	★★	★	★★★
simple	★★★	★★	★
data impossible	★★★	★★★	★

cache invalid-ize pattern

1. TTL (Time-To-Live)

# TTL configuration guide
CACHE_TTL = {
    "user_profile": 300,      # 5minutes — week change 
    "product_list": 60,       # 1minutes — between change frequency
    "stock_count": 10,        # 10seconds — week change
    "config": 3600,           # 1between — of change  
    "session": 86400,         # 24between — session countpeople
}

# Jitter addition (cache  )
import random
ttl = base_ttl + random.randint(0, base_ttl // 10)

2. Event-Based Invalidation

# event  invalid-ize
@event_handler("user.updated")
def invalidate_user_cache(event):
    cache.delete(f"user:{event.user_id}")
    cache.delete(f"user_profile:{event.user_id}")
    # related  cachealso invalid-ize
    cache.delete("user_list:page:*")

3. Version-Based

# before keyas in invalid-ize
version = cache.get("product_version") or 1
key = f"product_list:v{version}"
# invalid-ize: beforeonly 
cache.incr("product_version")

cache resolution

Cache Stampede (Thundering Herd)

: cache only   requestthis  DBin 
resolution:

1. Mutex Lock
   if cache.miss(key):
       if cache.lock(key + ":lock", timeout=5):
           result = db.query()
           cache.set(key, result, ttl)
           cache.unlock(key + ":lock")
       else:
           wait_and_retry()

2. Stale-While-Revalidate
   cache.set(key, data, ttl=300, stale_ttl=600)
   # TTL only afterinalso stale data lower the renewal

3. Probabilistic Early Expiry
   delta = ttl * beta * log(random())
   if now() - fetched_at > ttl + delta:
       refresh()

Cache Penetration

: lower  key repetition request →  DB 
resolution:
1.  filter:  possible before 
2.  result caching: cache.set(key, NULL, ttl=60)
3. request verification: validlower  key before 

Cache Avalanche

:  cache  only → DB andlower
resolution:
1. TTL variance: TTL + random jitter
2. phased only: importantalsoper different TTL
3. (Warm-up): deployment  cache before -based

cache layer

L1: as cache (process)
    ├── for: ~100MB
    ├── speed: ~0.1ms
    └── suitable: configuration, constant,  

L2: variance cache (Redis/Memcached)
    ├── for: ~10GB
    ├── speed: ~1ms
    └── suitable: session, as, 

L3: CDN cache (CloudFront/Cloudflare)
    ├── for: limited
    ├── speed: edgefrom ~5ms
    └── suitable: -based , API 

L4:  cache
    ├── Cache-Control: max-age, stale-while-revalidate
    └── ETag / Last-Modified

Redis data optional

	suitable	example
String	simple key-value	session, configuration,
Hash		user as
List		upper (LPUSH + LTRIM)
Set		user
Sorted Set		,
HyperLogLog		UV